Throughout this paper, we restricted our development of calibrated-\(\delta \)  adjustment MI to the case of a single partially observed covariate. However, we believe this approach can be extended for handling missing data in more than one variable. Multivariate imputation by chained equations (MICE) [1]}, [2]} is a popular procedure for performing MI of multivariate missing data, and is commonly implemented under the MAR assumption.[3]}, [4]} MICE is an iterative procedure which requires the specification of an imputation model for each incomplete variable, conditional on all other variables. Our proposed univariate calibrated-\(\delta \)  adjustment MI method can, in principle, be embedded into MICE to impute certain MNAR variables whose distributions are available externally, while the standard MI method can be used for the imputation of other variables assuming data are MAR. Under the MICE framework, when there are several MNAR variables to be imputed, information from more than one external data source can potentially be drawn on and utilised in calibrated-\(\delta \)  adjustment MI for these variables.
